In this script we conduct the estimation for the measure_marginal approach for a single given env = revm.

PROGRAMS=pg_marginal_full5_c50_step1_shuffle SAMPLESIZE=50 NSAMPLES=4.

Expected a result file revm_pg_marginal_full5_c50_step1_shuffle_50_4.csv.

programs = read.csv(paste("stage3/", program_set_codename, ".csv", sep=""))

results = load_data_set(env, program_set_codename, measurement_codename)
# besu may have additional columns with gc stats
results = results[, c("program_id", "sample_id", "run_id", "measure_total_time_ns", "measure_total_timer_time_ns", "env")]
# TODO geth short-circuits zero length programs, resulting in zero timing somehow. Drop these more elegantly, not based on measure_total_time_ns
results = results[which(results$measure_total_time_ns != 0), ]

all_envs = c(env)
measurements = sqldf("SELECT opcode, op_count, sample_id, run_id, measure_total_time_ns, env, results.program_id
                     FROM results
                     INNER JOIN
                       programs ON(results.program_id = programs.program_id)")
measurements$opcode = factor(measurements$opcode, levels=unique(programs$opcode))
head(measurements)
##   opcode op_count sample_id run_id measure_total_time_ns  env program_id
## 1    ADD       27         0      1                   581 revm     ADD_27
## 2    ADD       27         0      2                   585 revm     ADD_27
## 3    ADD       27         0      3                   633 revm     ADD_27
## 4    ADD       27         0      4                   610 revm     ADD_27
## 5    ADD       27         0      5                   563 revm     ADD_27
## 6    ADD       27         1      1                   588 revm     ADD_27

Switch removed_outliers to FALSE to see the comparison.

boxplot(measurements[which(measurements$env == env), 'measure_total_time_ns'] ~ measurements[which(measurements$env == env), 'opcode'], las=2, outline=TRUE, log='y', main=paste(env, 'all'))

if (removed_outliers) {
  measurements = remove_compare_outliers(measurements, 'measure_total_time_ns', all_envs)
}

# For a subset of the `measurements` data frame, fits a bimodal distribution model and corrects the
# data by bringing the "top-mode" cluster down to the "bottom-mode" cluster.
correct_bimodal <- function(df) {
  mix_model = normalmixEM(df$measure_total_time_ns)
  print(summary(mix_model))
  plot(mix_model,which=2)
  mode_distance = abs(mix_model$mu[2] - mix_model$mu[1])
  mode_midpoint = (mix_model$mu[2] + mix_model$mu[1]) / 2
  over_threshold = which(df$measure_total_time_ns > mode_midpoint)
  df[over_threshold, "measure_total_time_ns"] = df[over_threshold, "measure_total_time_ns"] - mode_distance
    
  return(df)
}

# Performs the `measure_marginal` estimation procedure for a given slice of the data.
# Prints the diagnostics and plots the models.
compute_all <- function(opcode, env, plots, bimodal_opcodes, use_median) {
  if (missing(bimodal_opcodes)) {
    bimodal_opcodes = c()
  }
  if (missing(plots)) {
    plots = "scatter"
  }
  if (missing(use_median)) {
    use_median = FALSE
  }
  print(c(opcode, env))
  
  df = measurements[which(measurements$opcode==opcode & measurements$env==env),]
  
  if (opcode %in% bimodal_opcodes) {
    par(mfrow=c(1,2))
    boxplot(measure_total_time_ns ~ op_count, data=df, las=2, outline=removed_outliers)
    title(main=paste(env, opcode))
    # correct_bimodal plots the second plot inside
    df = correct_bimodal(df)
  }
  
  if (use_median) {
    f = median
  } else {
    f = mean
  }
  df_mean = aggregate(measure_total_time_ns ~ op_count * env, df, f)
  
  model_mean = lm(measure_total_time_ns ~ op_count, data=df_mean)
  print(summary(model_mean))
  slope = model_mean$coefficients[['op_count']]
  stderr = summary(model_mean)$coefficients['op_count','Std. Error']
  
  if (plots == "scatter" | plots == "all") {
    par(mfrow=c(1,1))
    boxplot(measure_total_time_ns ~ op_count, data=df, las=2, outline=removed_outliers)
    rounded_slope = round(slope, 3)
    rounded_p = round(summary(model_mean)$coefficients['op_count','Pr(>|t|)'], 3)
    rounded_stderr = round(stderr, 3)
    title(main=paste(env, opcode, rounded_slope, "p_value:", rounded_p, "StdErr:", rounded_stderr))
    abline(model_mean, col="red")
  }
  if (plots == "diagnostics" | plots == "all") {
    par(mfrow=c(2,2))
    plot(model_mean)
  }
  list("slope" = slope, "stderr" = stderr)
}

extract_opcodes <- function() {
  unique(measurements$opcode)
}
all_opcodes = extract_opcodes()

# initialize the data frame to hold the results
estimates = data.frame(matrix(ncol = 4, nrow = 0))
colnames(estimates) <- c('op', 'estimate_marginal_ns', 'estimate_marginal_ns_stderr', 'env')

Every sample starts with a fresh evm instance. We investigate whether the results may depend on the time from evm start - related to run_id. To avoid being overrun by the number of images, all op_count for a given run_id are are placed, so values are not centered. That should not an issue.

for (opcode in all_opcodes) {
  boxplot(measure_total_time_ns~run_id,data=measurements[measurements$opcode == opcode,], main=opcode)
}

Now we can investigate the linear regressions.

if (env == 'evmone') {
  bimodals = all_opcodes[which(grepl("PUSH", all_opcodes) & all_opcodes != "PUSH1" | all_opcodes == "JUMP")]
} else {
  bimodals = c()
}

for (opcode in all_opcodes) {
  estimate = compute_all(opcode=opcode, env=env, use_median=TRUE, bimodal_opcodes=bimodals, plots='all')
  estimates[nrow(estimates) + 1, ] = c(opcode, estimate, env)
}
## [1] "ADD"  "revm"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -34.121  -1.291   1.827   4.709  12.590 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 519.12104    2.60124  199.57 <0.0000000000000002 ***
## op_count      2.06575    0.08966   23.04 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.425 on 49 degrees of freedom
## Multiple R-squared:  0.9155, Adjusted R-squared:  0.9138 
## F-statistic: 530.8 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "MUL"  "revm"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -33.173  -3.203   3.046   6.048  18.215 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 520.17270    2.75056  189.12 <0.0000000000000002 ***
## op_count      3.47231    0.09481   36.62 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.966 on 49 degrees of freedom
## Multiple R-squared:  0.9648, Adjusted R-squared:  0.964 
## F-statistic:  1341 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SUB"  "revm"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -34.308  -2.477   2.079   5.621  11.460 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 517.16139    2.48809  207.85 <0.0000000000000002 ***
## op_count      2.14688    0.08576   25.03 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.015 on 49 degrees of freedom
## Multiple R-squared:  0.9275, Adjusted R-squared:  0.926 
## F-statistic: 626.6 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DIV"  "revm"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -30.550  -5.052  -0.619   2.111  33.073 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 519.4770     3.3101  156.94 <0.0000000000000002 ***
## op_count      8.0731     0.1141   70.76 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.99 on 49 degrees of freedom
## Multiple R-squared:  0.9903, Adjusted R-squared:  0.9901 
## F-statistic:  5007 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SDIV" "revm"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -11.709  -3.424  -0.530   1.984  21.796 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 498.14367    1.56921   317.4 <0.0000000000000002 ***
## op_count     14.32367    0.05409   264.8 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.686 on 49 degrees of freedom
## Multiple R-squared:  0.9993, Adjusted R-squared:  0.9993 
## F-statistic: 7.013e+04 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "MOD"  "revm"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -30.109  -6.157  -1.000   3.456  32.264 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 517.6086     3.1497  164.33 <0.0000000000000002 ***
## op_count      8.3627     0.1086   77.03 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.41 on 49 degrees of freedom
## Multiple R-squared:  0.9918, Adjusted R-squared:  0.9916 
## F-statistic:  5933 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SMOD" "revm"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -11.1830  -4.7095  -0.2704   2.4026  21.3857 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 499.20023    1.88125   265.4 <0.0000000000000002 ***
## op_count     10.24140    0.06485   157.9 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.816 on 49 degrees of freedom
## Multiple R-squared:  0.998,  Adjusted R-squared:  0.998 
## F-statistic: 2.494e+04 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "ADDMOD" "revm"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9.0066 -2.7065  0.7806  3.0773  9.1612 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 493.88386    1.25811   392.6 <0.0000000000000002 ***
## op_count     15.15484    0.04337   349.5 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.559 on 49 degrees of freedom
## Multiple R-squared:  0.9996, Adjusted R-squared:  0.9996 
## F-statistic: 1.221e+05 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "MULMOD" "revm"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -30.490  -2.635   2.560   5.116  10.887 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 519.49020    2.13264   243.6 <0.0000000000000002 ***
## op_count     17.94471    0.07351   244.1 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.727 on 49 degrees of freedom
## Multiple R-squared:  0.9992, Adjusted R-squared:  0.9992 
## F-statistic: 5.959e+04 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "EXP"  "revm"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -20.8803  -3.1785   0.7508   5.5747  17.8022 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 503.28130    2.11720   237.7 <0.0000000000000002 ***
## op_count     18.74679    0.07298   256.9 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.671 on 49 degrees of freedom
## Multiple R-squared:  0.9993, Adjusted R-squared:  0.9992 
## F-statistic: 6.599e+04 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SIGNEXTEND" "revm"      
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -20.0965  -8.8114   0.9281   5.6708  24.9158 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 510.09653    2.87631   177.3 <0.0000000000000002 ***
## op_count      6.99928    0.09914    70.6 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10.42 on 49 degrees of freedom
## Multiple R-squared:  0.9903, Adjusted R-squared:  0.9901 
## F-statistic:  4984 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "LT"   "revm"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -31.045  -1.169   1.037   4.981  10.807 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 520.83861    2.37480  219.32 <0.0000000000000002 ***
## op_count      2.20606    0.08186   26.95 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.605 on 49 degrees of freedom
## Multiple R-squared:  0.9368, Adjusted R-squared:  0.9355 
## F-statistic: 726.3 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "GT"   "revm"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -28.847  -1.133   1.891   4.252  10.942 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 517.84691    2.20180  235.19 <0.0000000000000002 ***
## op_count      2.27005    0.07589   29.91 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.978 on 49 degrees of freedom
## Multiple R-squared:  0.9481, Adjusted R-squared:  0.947 
## F-statistic: 894.7 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SLT"  "revm"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -28.962  -3.411   1.838   4.989  10.539 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 514.46192    2.14295  240.07 <0.0000000000000002 ***
## op_count      3.79995    0.07387   51.44 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.765 on 49 degrees of freedom
## Multiple R-squared:  0.9818, Adjusted R-squared:  0.9815 
## F-statistic:  2647 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SGT"  "revm"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -35.647  -2.117   2.093   4.423  11.973 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 517.64706    2.38730  216.83 <0.0000000000000002 ***
## op_count      3.78000    0.08229   45.94 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.65 on 49 degrees of freedom
## Multiple R-squared:  0.9773, Adjusted R-squared:  0.9768 
## F-statistic:  2110 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "EQ"   "revm"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -30.930  -2.431   2.613   5.018  12.658 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 521.0038     2.5415  205.00 <0.0000000000000002 ***
## op_count      1.9265     0.0876   21.99 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.209 on 49 degrees of freedom
## Multiple R-squared:  0.908,  Adjusted R-squared:  0.9061 
## F-statistic: 483.6 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "ISZERO" "revm"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -24.9721  -3.3104   0.7074   5.3546  13.7170 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 513.97210    2.15655  238.33 <0.0000000000000002 ***
## op_count      1.83014    0.07433   24.62 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.814 on 49 degrees of freedom
## Multiple R-squared:  0.9252, Adjusted R-squared:  0.9237 
## F-statistic: 606.2 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "AND"  "revm"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -30.876  -2.347   2.279   4.566  18.726 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 519.87632    2.59596  200.26 <0.0000000000000002 ***
## op_count      1.67475    0.08948   18.72 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.406 on 49 degrees of freedom
## Multiple R-squared:  0.8773, Adjusted R-squared:  0.8748 
## F-statistic: 350.3 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "OR"   "revm"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -36.045  -2.106   2.777   6.017  12.258 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 518.37443    2.77529  186.78 <0.0000000000000002 ***
## op_count      1.67090    0.09566   17.47 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10.06 on 49 degrees of freedom
## Multiple R-squared:  0.8616, Adjusted R-squared:  0.8588 
## F-statistic: 305.1 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "XOR"  "revm"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -33.457  -2.907   2.056   5.328  16.796 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  520.836      2.727  190.99 <0.0000000000000002 ***
## op_count       1.620      0.094   17.24 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.881 on 49 degrees of freedom
## Multiple R-squared:  0.8584, Adjusted R-squared:  0.8555 
## F-statistic: 297.1 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "NOT"  "revm"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -28.339  -1.672   2.108   5.665  12.789 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 515.89894    2.60336  198.17 <0.0000000000000002 ***
## op_count      1.43973    0.08974   16.04 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.433 on 49 degrees of freedom
## Multiple R-squared:  0.8401, Adjusted R-squared:  0.8368 
## F-statistic: 257.4 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "BYTE" "revm"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -31.1018  -8.9156  -0.0614   4.8837  30.7727 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  518.602      3.860   134.4 <0.0000000000000002 ***
## op_count       5.535      0.133    41.6 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13.99 on 49 degrees of freedom
## Multiple R-squared:  0.9725, Adjusted R-squared:  0.9719 
## F-statistic:  1731 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SHL"  "revm"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -53.516 -10.291  -5.858  17.772  29.504 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 539.9593     5.4893   98.37 <0.0000000000000002 ***
## op_count      6.0569     0.1892   32.01 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 19.89 on 49 degrees of freedom
## Multiple R-squared:  0.9544, Adjusted R-squared:  0.9534 
## F-statistic:  1025 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SHR"  "revm"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -36.956  -6.963  -1.781   5.212  34.825 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 523.3171     4.3985  118.98 <0.0000000000000002 ***
## op_count      7.6387     0.1516   50.38 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 15.94 on 49 degrees of freedom
## Multiple R-squared:  0.9811, Adjusted R-squared:  0.9807 
## F-statistic:  2538 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SAR"  "revm"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -15.074  -5.192  -2.758   2.818  27.921 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 503.52828    2.43796   206.5 <0.0000000000000002 ***
## op_count     10.04593    0.08403   119.5 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.834 on 49 degrees of freedom
## Multiple R-squared:  0.9966, Adjusted R-squared:  0.9965 
## F-statistic: 1.429e+04 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "ADDRESS" "revm"   
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -30.705  -1.856   2.833   5.375   9.753 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 226.8940     2.4020   94.46 <0.0000000000000002 ***
## op_count      1.8109     0.0828   21.87 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.703 on 49 degrees of freedom
## Multiple R-squared:  0.9071, Adjusted R-squared:  0.9052 
## F-statistic: 478.4 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "ORIGIN" "revm"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -29.698  -1.348   3.328   5.359   8.932 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 225.70626    2.37148   95.17 <0.0000000000000002 ***
## op_count      1.99136    0.08174   24.36 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.593 on 49 degrees of freedom
## Multiple R-squared:  0.9237, Adjusted R-squared:  0.9222 
## F-statistic: 593.5 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "CALLER" "revm"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -31.323  -1.944   3.422   6.291   8.644 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 228.32278    2.55540   89.35 <0.0000000000000002 ***
## op_count      1.83611    0.08808   20.84 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.259 on 49 degrees of freedom
## Multiple R-squared:  0.8987, Adjusted R-squared:  0.8966 
## F-statistic: 434.5 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "CALLVALUE" "revm"     
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -31.748  -1.372   2.525   5.013   9.735 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 227.74774    2.57177   88.56 <0.0000000000000002 ***
## op_count      1.94538    0.08865   21.95 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.318 on 49 degrees of freedom
## Multiple R-squared:  0.9077, Adjusted R-squared:  0.9058 
## F-statistic: 481.6 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "CALLDATALOAD" "revm"        
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -50.302 -11.395   0.417  14.068  35.258 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 792.3017     4.8360  163.84 <0.0000000000000002 ***
## op_count      2.9801     0.1667   17.88 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 17.52 on 49 degrees of freedom
## Multiple R-squared:  0.8671, Adjusted R-squared:  0.8644 
## F-statistic: 319.6 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "CALLDATASIZE" "revm"        
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -30.7357  -0.7218   2.3999   5.1869   9.1495 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 227.73567    2.41254   94.40 <0.0000000000000002 ***
## op_count      1.58783    0.08316   19.09 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.741 on 49 degrees of freedom
## Multiple R-squared:  0.8815, Adjusted R-squared:  0.8791 
## F-statistic: 364.6 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "CALLDATACOPY" "revm"        
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -20.974  -7.694  -0.370   6.433  37.010 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  508.701      2.929  173.67 <0.0000000000000002 ***
## op_count       5.152      0.101   51.02 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10.61 on 49 degrees of freedom
## Multiple R-squared:  0.9815, Adjusted R-squared:  0.9811 
## F-statistic:  2603 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "CODESIZE" "revm"    
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -28.489  -1.339   2.311   5.147  10.625 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 225.98944    2.40859   93.83 <0.0000000000000002 ***
## op_count      1.67140    0.08302   20.13 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.727 on 49 degrees of freedom
## Multiple R-squared:  0.8921, Adjusted R-squared:  0.8899 
## F-statistic: 405.3 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "CODECOPY" "revm"    
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -14.731  -5.514  -1.131   3.252  27.835 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 520.76508    2.31876  224.59 <0.0000000000000002 ***
## op_count      6.76665    0.07993   84.66 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.402 on 49 degrees of freedom
## Multiple R-squared:  0.9932, Adjusted R-squared:  0.9931 
## F-statistic:  7168 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "GASPRICE" "revm"    
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -28.655  -1.324   2.770   5.262  10.783 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 225.65083    2.44318   92.36 <0.0000000000000002 ***
## op_count      2.00416    0.08421   23.80 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.853 on 49 degrees of freedom
## Multiple R-squared:  0.9204, Adjusted R-squared:  0.9187 
## F-statistic: 566.4 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "RETURNDATASIZE" "revm"          
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -32.272  -1.286   3.137   5.015  10.257 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 227.66365    2.56675   88.70 <0.0000000000000002 ***
## op_count      1.60796    0.08847   18.18 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.3 on 49 degrees of freedom
## Multiple R-squared:  0.8708, Adjusted R-squared:  0.8682 
## F-statistic: 330.3 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "COINBASE" "revm"    
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -30.451  -1.148   1.582   5.153  10.071 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 226.61840    2.37815   95.29 <0.0000000000000002 ***
## op_count      1.83213    0.08197   22.35 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.617 on 49 degrees of freedom
## Multiple R-squared:  0.9107, Adjusted R-squared:  0.9088 
## F-statistic: 499.5 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "TIMESTAMP" "revm"     
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -27.788  -1.344   2.398   4.808   8.187 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 224.82428    2.17733  103.26 <0.0000000000000002 ***
## op_count      1.96389    0.07505   26.17 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.889 on 49 degrees of freedom
## Multiple R-squared:  0.9332, Adjusted R-squared:  0.9319 
## F-statistic: 684.7 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "NUMBER" "revm"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -30.319  -1.115   3.463   5.213  10.994 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 226.35030    2.52599   89.61 <0.0000000000000002 ***
## op_count      1.96873    0.08707   22.61 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.153 on 49 degrees of freedom
## Multiple R-squared:  0.9125, Adjusted R-squared:  0.9108 
## F-statistic: 511.3 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DIFFICULTY" "revm"      
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -29.975  -1.427   2.718   4.716   9.490 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 226.87255    2.32526   97.57 <0.0000000000000002 ***
## op_count      2.10235    0.08015   26.23 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.425 on 49 degrees of freedom
## Multiple R-squared:  0.9335, Adjusted R-squared:  0.9322 
## F-statistic:   688 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "GASLIMIT" "revm"    
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -29.8752  -0.5465   2.5777   5.0714  10.1531 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 226.87519    2.51150   90.33 <0.0000000000000002 ***
## op_count      1.95597    0.08657   22.59 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.1 on 49 degrees of freedom
## Multiple R-squared:  0.9124, Adjusted R-squared:  0.9106 
## F-statistic: 510.5 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "CHAINID" "revm"   
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -29.929  -1.649   3.316   5.345  10.631 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 226.92911    2.55791   88.72 <0.0000000000000002 ***
## op_count      1.93774    0.08817   21.98 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.268 on 49 degrees of freedom
## Multiple R-squared:  0.9079, Adjusted R-squared:  0.906 
## F-statistic:   483 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SELFBALANCE" "revm"       
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -28.1909  -0.5871   2.0552   4.3245  12.5798 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 224.56938    2.19888  102.13 <0.0000000000000002 ***
## op_count      1.62154    0.07579   21.39 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.967 on 49 degrees of freedom
## Multiple R-squared:  0.9033, Adjusted R-squared:  0.9013 
## F-statistic: 457.7 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "POP"  "revm"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.9024 -2.6426 -0.0837  1.8050  7.2963 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 317.28017    0.85989  368.98 <0.0000000000000002 ***
## op_count      1.33624    0.02964   45.08 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.116 on 49 degrees of freedom
## Multiple R-squared:  0.9765, Adjusted R-squared:  0.976 
## F-statistic:  2032 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "MLOAD" "revm" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -33.090 -10.175   0.051  10.930  26.981 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 784.9099     4.3012  182.49 <0.0000000000000002 ***
## op_count      6.1797     0.1483   41.68 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 15.58 on 49 degrees of freedom
## Multiple R-squared:  0.9726, Adjusted R-squared:  0.972 
## F-statistic:  1737 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "MSTORE" "revm"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -20.943  -8.515   1.290   6.228  29.142 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 512.8296     3.0684   167.1 <0.0000000000000002 ***
## op_count      3.4476     0.1058    32.6 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.12 on 49 degrees of freedom
## Multiple R-squared:  0.9559, Adjusted R-squared:  0.955 
## F-statistic:  1063 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "MSTORE8" "revm"   
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -20.1791  -7.8512  -0.6557   7.6705  28.7647 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 506.6357     3.3110  153.02 <0.0000000000000002 ***
## op_count      2.9040     0.1141   25.45 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 12 on 49 degrees of freedom
## Multiple R-squared:  0.9296, Adjusted R-squared:  0.9282 
## F-statistic: 647.5 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "JUMP" "revm"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -12.1452  -1.4236   0.2671   1.7929  10.2741 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 195.14517    1.14211  170.86 <0.0000000000000002 ***
## op_count      2.31615    0.03937   58.83 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.138 on 49 degrees of freedom
## Multiple R-squared:  0.986,  Adjusted R-squared:  0.9858 
## F-statistic:  3461 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "JUMPI" "revm" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -37.826 -10.544  -1.831  13.502  23.452 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 520.8262     4.1957  124.13 <0.0000000000000002 ***
## op_count      2.7901     0.1446   19.29 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 15.2 on 49 degrees of freedom
## Multiple R-squared:  0.8837, Adjusted R-squared:  0.8813 
## F-statistic: 372.2 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PC"   "revm"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -30.706  -0.395   2.463   4.724   9.937 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 227.1112     2.3876   95.12 <0.0000000000000002 ***
## op_count      1.5952     0.0823   19.38 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.651 on 49 degrees of freedom
## Multiple R-squared:  0.8846, Adjusted R-squared:  0.8823 
## F-statistic: 375.7 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "MSIZE" "revm" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -30.998  -2.426   3.105   5.922   9.435 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 227.82353    2.56135   88.95 <0.0000000000000002 ***
## op_count      1.67412    0.08829   18.96 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.281 on 49 degrees of freedom
## Multiple R-squared:  0.8801, Adjusted R-squared:  0.8776 
## F-statistic: 359.6 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "GAS"  "revm"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -30.3722  -0.7895   2.2842   5.0148  11.5395 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 226.77413    2.33931   96.94 <0.0000000000000002 ***
## op_count      1.59805    0.08063   19.82 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.476 on 49 degrees of freedom
## Multiple R-squared:  0.8891, Adjusted R-squared:  0.8868 
## F-statistic: 392.8 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "JUMPDEST" "revm"    
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -5.833 -3.169 -0.034  1.421 47.719 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 16.28054    2.04377   7.966       0.000000000215 ***
## op_count     1.09701    0.07045  15.572 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.405 on 49 degrees of freedom
## Multiple R-squared:  0.8319, Adjusted R-squared:  0.8285 
## F-statistic: 242.5 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH1" "revm" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -14.077  -3.820   1.560   4.331  12.608 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 205.89857    1.77408  116.06 <0.0000000000000002 ***
## op_count      1.53425    0.06115   25.09 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.428 on 49 degrees of freedom
## Multiple R-squared:  0.9278, Adjusted R-squared:  0.9263 
## F-statistic: 629.5 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH2" "revm" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -31.388  -2.400   2.029   6.009  11.273 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 225.83107    2.45282   92.07 <0.0000000000000002 ***
## op_count      1.55656    0.08455   18.41 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.887 on 49 degrees of freedom
## Multiple R-squared:  0.8737, Adjusted R-squared:  0.8711 
## F-statistic:   339 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH3" "revm" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -32.431  -2.450   2.863   6.201  11.054 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 226.78620    2.60961   86.90 <0.0000000000000002 ***
## op_count      1.64502    0.08995   18.29 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.456 on 49 degrees of freedom
## Multiple R-squared:  0.8722, Adjusted R-squared:  0.8696 
## F-statistic: 334.5 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH4" "revm" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -32.255  -1.631   3.264   5.615  11.504 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 227.64932    2.46391   92.39 <0.0000000000000002 ***
## op_count      1.60579    0.08493   18.91 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.928 on 49 degrees of freedom
## Multiple R-squared:  0.8795, Adjusted R-squared:  0.877 
## F-statistic: 357.5 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH5" "revm" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -33.714  -1.070   2.226   5.167  10.404 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 227.06637    2.51094   90.43 <0.0000000000000002 ***
## op_count      1.64715    0.08655   19.03 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.098 on 49 degrees of freedom
## Multiple R-squared:  0.8808, Adjusted R-squared:  0.8784 
## F-statistic: 362.2 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH6" "revm" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -31.514  -2.590   2.877   5.212  11.154 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 226.87481    2.43061   93.34 <0.0000000000000002 ***
## op_count      1.63873    0.08378   19.56 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.807 on 49 degrees of freedom
## Multiple R-squared:  0.8865, Adjusted R-squared:  0.8841 
## F-statistic: 382.6 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH7" "revm" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -33.363  -0.867   2.133   5.102   9.802 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 227.60671    2.46705   92.26 <0.0000000000000002 ***
## op_count      1.75652    0.08504   20.66 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.939 on 49 degrees of freedom
## Multiple R-squared:  0.897,  Adjusted R-squared:  0.8949 
## F-statistic: 426.7 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH8" "revm" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -33.390  -0.732   1.901   5.649  10.398 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 227.84276    2.54915   89.38 <0.0000000000000002 ***
## op_count      1.54747    0.08787   17.61 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.236 on 49 degrees of freedom
## Multiple R-squared:  0.8636, Adjusted R-squared:  0.8608 
## F-statistic: 310.2 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH9" "revm" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -29.027  -1.491   2.774   6.157  11.152 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 223.40988    2.43287   91.83 <0.0000000000000002 ***
## op_count      1.61733    0.08386   19.29 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.815 on 49 degrees of freedom
## Multiple R-squared:  0.8836, Adjusted R-squared:  0.8812 
## F-statistic:   372 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH10" "revm"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -35.023  -4.333   3.359   5.811   9.955 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 228.38499    2.71567    84.1 <0.0000000000000002 ***
## op_count      1.63833    0.09361    17.5 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.84 on 49 degrees of freedom
## Multiple R-squared:  0.8621, Adjusted R-squared:  0.8593 
## F-statistic: 306.3 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH11" "revm"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -31.184  -1.681   2.717   4.118  11.242 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 226.42157    2.40630   94.09 <0.0000000000000002 ***
## op_count      1.76235    0.08294   21.25 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.719 on 49 degrees of freedom
## Multiple R-squared:  0.9021, Adjusted R-squared:  0.9001 
## F-statistic: 451.5 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH12" "revm"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -31.261  -3.718   2.833   5.284  12.104 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 226.7413     2.3412   96.85 <0.0000000000000002 ***
## op_count      1.5194     0.0807   18.83 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.483 on 49 degrees of freedom
## Multiple R-squared:  0.8786, Adjusted R-squared:  0.8761 
## F-statistic: 354.5 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH13" "revm"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -29.962  -1.447   2.036   5.323  10.370 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 226.20211    2.35098   96.22 <0.0000000000000002 ***
## op_count      1.75937    0.08104   21.71 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.518 on 49 degrees of freedom
## Multiple R-squared:  0.9058, Adjusted R-squared:  0.9039 
## F-statistic: 471.4 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH14" "revm"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -31.498  -1.754   2.509   5.779  10.276 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 226.71078    2.44818   92.60 <0.0000000000000002 ***
## op_count      1.78765    0.08439   21.18 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.871 on 49 degrees of freedom
## Multiple R-squared:  0.9016, Adjusted R-squared:  0.8996 
## F-statistic: 448.8 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH15" "revm"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -31.969  -1.701   2.390   5.620  10.151 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 226.98906    2.51076   90.41 <0.0000000000000002 ***
## op_count      1.98005    0.08654   22.88 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.097 on 49 degrees of freedom
## Multiple R-squared:  0.9144, Adjusted R-squared:  0.9127 
## F-statistic: 523.5 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH16" "revm"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -31.188  -1.343   2.381   5.387   8.872 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 226.57994    2.40318   94.28 <0.0000000000000002 ***
## op_count      1.60778    0.08284   19.41 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.708 on 49 degrees of freedom
## Multiple R-squared:  0.8849, Adjusted R-squared:  0.8826 
## F-statistic: 376.7 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH17" "revm"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -33.956  -1.093   1.732   5.943  10.132 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 227.18514    2.51476   90.34 <0.0000000000000002 ***
## op_count      1.77063    0.08668   20.43 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.112 on 49 degrees of freedom
## Multiple R-squared:  0.8949, Adjusted R-squared:  0.8928 
## F-statistic: 417.3 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH18" "revm"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -31.969  -2.667   1.481   5.295  12.158 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 226.70098    2.44512   92.72 <0.0000000000000002 ***
## op_count      1.76765    0.08428   20.97 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.86 on 49 degrees of freedom
## Multiple R-squared:  0.8998, Adjusted R-squared:  0.8977 
## F-statistic: 439.9 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH19" "revm"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -29.016  -1.583   2.122   4.427  10.760 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 224.06222    2.11345  106.02 <0.0000000000000002 ***
## op_count      1.95398    0.07285   26.82 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.658 on 49 degrees of freedom
## Multiple R-squared:  0.9362, Adjusted R-squared:  0.9349 
## F-statistic: 719.4 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH20" "revm"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -29.346  -2.926   2.182   5.293  11.685 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 225.56410    2.40172   93.92 <0.0000000000000002 ***
## op_count      1.78136    0.08279   21.52 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.702 on 49 degrees of freedom
## Multiple R-squared:  0.9043, Adjusted R-squared:  0.9023 
## F-statistic:   463 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH21" "revm"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -30.215  -1.524   1.601   4.402  14.865 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 224.29789    2.22271  100.91 <0.0000000000000002 ***
## op_count      1.91710    0.07661   25.02 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.054 on 49 degrees of freedom
## Multiple R-squared:  0.9274, Adjusted R-squared:  0.9259 
## F-statistic: 626.1 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH22" "revm"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -29.713  -2.124   1.599   4.691  13.723 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 224.77564    2.36613   95.00 <0.0000000000000002 ***
## op_count      1.93760    0.08156   23.76 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.573 on 49 degrees of freedom
## Multiple R-squared:  0.9201, Adjusted R-squared:  0.9185 
## F-statistic: 564.4 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH23" "revm"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -30.449  -2.379   2.223   4.422  15.836 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 225.48982    2.35138   95.90 <0.0000000000000002 ***
## op_count      1.95923    0.08105   24.17 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.52 on 49 degrees of freedom
## Multiple R-squared:  0.9226, Adjusted R-squared:  0.9211 
## F-statistic: 584.3 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH24" "revm"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -30.928  -1.651   2.499   5.191  10.409 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 226.92760    2.41719   93.88 <0.0000000000000002 ***
## op_count      1.80525    0.08332   21.67 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.758 on 49 degrees of freedom
## Multiple R-squared:  0.9055, Adjusted R-squared:  0.9036 
## F-statistic: 469.5 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH25" "revm"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -30.244  -3.048   2.517   4.950  11.508 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 224.36991    2.23326  100.47 <0.0000000000000002 ***
## op_count      1.87462    0.07698   24.35 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.092 on 49 degrees of freedom
## Multiple R-squared:  0.9237, Adjusted R-squared:  0.9221 
## F-statistic:   593 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH26" "revm"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -29.145  -1.998   2.715   4.514   9.727 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 224.62406    2.19385  102.39 <0.0000000000000002 ***
## op_count      2.02131    0.07562   26.73 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.949 on 49 degrees of freedom
## Multiple R-squared:  0.9358, Adjusted R-squared:  0.9345 
## F-statistic: 714.5 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH27" "revm"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -29.863  -1.280   1.804   4.443  12.804 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 224.80694    2.26160   99.40 <0.0000000000000002 ***
## op_count      2.05557    0.07796   26.37 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.195 on 49 degrees of freedom
## Multiple R-squared:  0.9342, Adjusted R-squared:  0.9328 
## F-statistic: 695.3 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH28" "revm"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -30.544  -3.219   1.519   4.752  13.624 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 224.57164    2.37095   94.72 <0.0000000000000002 ***
## op_count      1.97204    0.08172   24.13 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.591 on 49 degrees of freedom
## Multiple R-squared:  0.9224, Adjusted R-squared:  0.9208 
## F-statistic: 582.3 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH29" "revm"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -29.385  -2.180   2.523   5.464  11.696 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 225.31561    2.38070   94.64 <0.0000000000000002 ***
## op_count      2.06973    0.08206   25.22 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.626 on 49 degrees of freedom
## Multiple R-squared:  0.9285, Adjusted R-squared:  0.927 
## F-statistic: 636.1 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH30" "revm"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -31.715  -1.859   2.118   4.931  11.822 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 225.63763    2.47058   91.33 <0.0000000000000002 ***
## op_count      2.07724    0.08516   24.39 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.952 on 49 degrees of freedom
## Multiple R-squared:  0.9239, Adjusted R-squared:  0.9224 
## F-statistic:   595 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH31" "revm"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -31.766  -2.552   2.312   5.422  13.228 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 226.07127    2.54357   88.88 <0.0000000000000002 ***
## op_count      2.19480    0.08767   25.03 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.216 on 49 degrees of freedom
## Multiple R-squared:  0.9275, Adjusted R-squared:  0.926 
## F-statistic: 626.7 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH32" "revm"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -28.546  -1.925   2.542   4.547  13.473 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 223.54864    2.24372   99.63 <0.0000000000000002 ***
## op_count      1.99688    0.07734   25.82 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.13 on 49 degrees of freedom
## Multiple R-squared:  0.9315, Adjusted R-squared:  0.9301 
## F-statistic: 666.7 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP1" "revm"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -30.430  -1.652   2.168   5.620  11.755 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 516.83974    2.47344  208.96 <0.0000000000000002 ***
## op_count      1.59072    0.08526   18.66 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.962 on 49 degrees of freedom
## Multiple R-squared:  0.8766, Adjusted R-squared:  0.8741 
## F-statistic: 348.1 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP2" "revm"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -33.065  -3.517   2.636   5.404  12.548 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 519.42081    2.69662  192.62 <0.0000000000000002 ***
## op_count      1.64434    0.09295   17.69 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.771 on 49 degrees of freedom
## Multiple R-squared:  0.8646, Adjusted R-squared:  0.8619 
## F-statistic:   313 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP3" "revm"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -29.929  -2.996   2.792   5.702  13.024 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 518.92911    2.65958  195.12 <0.0000000000000002 ***
## op_count      1.64950    0.09167   17.99 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.637 on 49 degrees of freedom
## Multiple R-squared:  0.8685, Adjusted R-squared:  0.8659 
## F-statistic: 323.8 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP4" "revm"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -33.242  -1.838   2.713   6.617  12.722 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 520.24170    2.79124  186.38 <0.0000000000000002 ***
## op_count      1.58484    0.09621   16.47 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10.11 on 49 degrees of freedom
## Multiple R-squared:  0.847,  Adjusted R-squared:  0.8439 
## F-statistic: 271.3 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP5" "revm"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -33.612  -2.122   2.410   5.208  15.939 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 519.58258    2.64165   196.7 <0.0000000000000002 ***
## op_count      1.52964    0.09106    16.8 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.572 on 49 degrees of freedom
## Multiple R-squared:  0.8521, Adjusted R-squared:  0.849 
## F-statistic: 282.2 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP6" "revm"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -32.064  -1.898   1.443   5.820  15.822 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 521.44683    2.73141  190.91 <0.0000000000000002 ***
## op_count      1.61742    0.09415   17.18 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.897 on 49 degrees of freedom
## Multiple R-squared:  0.8576, Adjusted R-squared:  0.8547 
## F-statistic: 295.1 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP7" "revm"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -32.094  -1.644   2.650   5.393  11.320 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 525.53017    2.70722  194.12 <0.0000000000000002 ***
## op_count      1.56389    0.09332   16.76 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.809 on 49 degrees of freedom
## Multiple R-squared:  0.8515, Adjusted R-squared:  0.8484 
## F-statistic: 280.9 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP8" "revm"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -30.908  -1.995   2.331   6.136  13.418 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 525.38575    2.65117  198.17 <0.0000000000000002 ***
## op_count      1.63045    0.09138   17.84 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.606 on 49 degrees of freedom
## Multiple R-squared:  0.8666, Adjusted R-squared:  0.8639 
## F-statistic: 318.3 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP9" "revm"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -35.131  -1.029   2.073   5.909  12.937 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 519.50038    2.86215  181.51 <0.0000000000000002 ***
## op_count      1.63018    0.09866   16.52 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10.37 on 49 degrees of freedom
## Multiple R-squared:  0.8478, Adjusted R-squared:  0.8447 
## F-statistic:   273 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP10" "revm" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -34.959  -2.937   2.354   5.141  13.548 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 518.31750    2.73706   189.4 <0.0000000000000002 ***
## op_count      1.64181    0.09434    17.4 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.917 on 49 degrees of freedom
## Multiple R-squared:  0.8607, Adjusted R-squared:  0.8579 
## F-statistic: 302.8 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP11" "revm" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -34.011  -1.606   2.798   5.516  15.804 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 533.41176    2.77220  192.41 <0.0000000000000002 ***
## op_count      1.59882    0.09556   16.73 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10.04 on 49 degrees of freedom
## Multiple R-squared:  0.851,  Adjusted R-squared:  0.848 
## F-statistic:   280 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP12" "revm" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -33.487  -3.027   2.848   5.979  17.924 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 521.48680    2.72940  191.06 <0.0000000000000002 ***
## op_count      1.63190    0.09408   17.35 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.89 on 49 degrees of freedom
## Multiple R-squared:   0.86,  Adjusted R-squared:  0.8571 
## F-statistic: 300.9 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP13" "revm" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -32.857  -2.760   4.479   6.853  13.133 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 522.66365    2.84803  183.52 <0.0000000000000002 ***
## op_count      1.69385    0.09817   17.25 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10.32 on 49 degrees of freedom
## Multiple R-squared:  0.8587, Adjusted R-squared:  0.8558 
## F-statistic: 297.7 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP14" "revm" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -35.586  -2.595   2.848   5.848  12.053 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 522.93854    2.61641  199.87 <0.0000000000000002 ***
## op_count      1.64756    0.09019   18.27 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.48 on 49 degrees of freedom
## Multiple R-squared:  0.872,  Adjusted R-squared:  0.8694 
## F-statistic: 333.7 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP15" "revm" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -32.057  -1.980   2.391   6.036  12.222 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 519.43967    2.64511  196.38 <0.0000000000000002 ***
## op_count      1.61692    0.09117   17.73 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.584 on 49 degrees of freedom
## Multiple R-squared:  0.8652, Adjusted R-squared:  0.8625 
## F-statistic: 314.5 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP16" "revm" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -40.698  -2.365   3.358   5.830  12.257 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 538.69796    2.85948  188.39 <0.0000000000000002 ***
## op_count      1.64973    0.09856   16.74 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10.36 on 49 degrees of freedom
## Multiple R-squared:  0.8511, Adjusted R-squared:  0.8481 
## F-statistic: 280.1 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP1" "revm" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -8.3398 -2.6690 -0.4617  2.6236  9.9651 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 320.04713    1.02181  313.22 <0.0000000000000002 ***
## op_count      2.52439    0.03522   71.67 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.702 on 49 degrees of freedom
## Multiple R-squared:  0.9906, Adjusted R-squared:  0.9904 
## F-statistic:  5137 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP2" "revm" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.5042 -1.6168 -0.0011  1.2335  7.0545 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 317.31900    0.81103  391.25 <0.0000000000000002 ***
## op_count      2.44136    0.02796   87.33 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.939 on 49 degrees of freedom
## Multiple R-squared:  0.9936, Adjusted R-squared:  0.9935 
## F-statistic:  7627 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP3" "revm" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4542 -2.6289 -0.9463  1.8731  7.7362 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 318.95814    0.88677   359.7 <0.0000000000000002 ***
## op_count      2.40873    0.03057    78.8 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.213 on 49 degrees of freedom
## Multiple R-squared:  0.9922, Adjusted R-squared:  0.992 
## F-statistic:  6210 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP4" "revm" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.9922 -3.1373 -0.3989  2.2747  7.9871 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 319.3782     1.0589  301.61 <0.0000000000000002 ***
## op_count      2.5104     0.0365   68.78 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.837 on 49 degrees of freedom
## Multiple R-squared:  0.9897, Adjusted R-squared:  0.9895 
## F-statistic:  4730 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP5" "revm" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -8.2759 -1.9118  0.1695  2.1155  6.6218 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 318.86689    0.87527  364.31 <0.0000000000000002 ***
## op_count      2.56023    0.03017   84.86 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.171 on 49 degrees of freedom
## Multiple R-squared:  0.9932, Adjusted R-squared:  0.9931 
## F-statistic:  7201 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP6" "revm" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.3333 -2.3848 -0.5686  1.9093  9.3284 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 321.81863    0.91290  352.52 <0.0000000000000002 ***
## op_count      2.45588    0.03147   78.05 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.308 on 49 degrees of freedom
## Multiple R-squared:  0.992,  Adjusted R-squared:  0.9919 
## F-statistic:  6091 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP7" "revm" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9.0068 -2.5101 -0.5097  2.7415  7.9907 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 328.51320    1.06616  308.13 <0.0000000000000002 ***
## op_count      2.49986    0.03675   68.02 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.863 on 49 degrees of freedom
## Multiple R-squared:  0.9895, Adjusted R-squared:  0.9893 
## F-statistic:  4627 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP8" "revm" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.8839 -1.6780 -0.2345  1.8051 13.8899 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 320.88386    1.05934  302.91 <0.0000000000000002 ***
## op_count      2.51131    0.03651   68.78 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.838 on 49 degrees of freedom
## Multiple R-squared:  0.9897, Adjusted R-squared:  0.9895 
## F-statistic:  4730 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP9" "revm" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.8460 -2.4921 -0.7775  1.9083 15.4694 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 320.79827    1.20200  266.89 <0.0000000000000002 ***
## op_count      2.56846    0.04143   61.99 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.355 on 49 degrees of freedom
## Multiple R-squared:  0.9874, Adjusted R-squared:  0.9872 
## F-statistic:  3843 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP10" "revm"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.4906 -3.7293 -0.8214  2.2406 21.1880 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 331.13348    1.55859  212.46 <0.0000000000000002 ***
## op_count      2.61466    0.05372   48.67 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.647 on 49 degrees of freedom
## Multiple R-squared:  0.9797, Adjusted R-squared:  0.9793 
## F-statistic:  2369 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP11" "revm"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.1051 -3.2954  0.1944  1.9686 13.5777 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 320.4148     1.2389  258.63 <0.0000000000000002 ***
## op_count      2.3909     0.0427   55.99 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.489 on 49 degrees of freedom
## Multiple R-squared:  0.9846, Adjusted R-squared:  0.9843 
## F-statistic:  3135 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP12" "revm"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.4487 -2.4381 -0.6198  1.7750 15.5619 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 325.15913    1.03716  313.51 <0.0000000000000002 ***
## op_count      2.36579    0.03575   66.18 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.758 on 49 degrees of freedom
## Multiple R-squared:  0.9889, Adjusted R-squared:  0.9887 
## F-statistic:  4379 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP13" "revm"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9.9778 -2.8414  0.3338  2.8206  9.5503 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 325.97775    1.15361  282.57 <0.0000000000000002 ***
## op_count      2.54050    0.03976   63.89 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.18 on 49 degrees of freedom
## Multiple R-squared:  0.9881, Adjusted R-squared:  0.9879 
## F-statistic:  4082 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP14" "revm"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.534 -3.026 -0.557  3.178  9.414 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 319.11727    1.14505  278.69 <0.0000000000000002 ***
## op_count      2.49805    0.03947   63.29 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.149 on 49 degrees of freedom
## Multiple R-squared:  0.9879, Adjusted R-squared:  0.9877 
## F-statistic:  4006 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP15" "revm"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9.9502 -2.5611 -0.4977  2.7095 11.0213 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 338.95023    1.23997  273.35 <0.0000000000000002 ***
## op_count      2.50317    0.04274   58.57 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.493 on 49 degrees of freedom
## Multiple R-squared:  0.9859, Adjusted R-squared:  0.9856 
## F-statistic:  3430 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP16" "revm"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.4419 -3.2526 -0.5228  3.2609 11.8960 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 331.44193    1.23791  267.74 <0.0000000000000002 ***
## op_count      2.43919    0.04267   57.16 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.485 on 49 degrees of freedom
## Multiple R-squared:  0.9852, Adjusted R-squared:  0.9849 
## F-statistic:  3268 on 1 and 49 DF,  p-value: < 0.00000000000000022

Export the results

write.csv(estimates, paste0("../../local/", env, "_marginal_estimated_cost.csv"), quote=FALSE, row.names=FALSE)